Home automation systems, or smart homes, offer a wide variety of functions. Some include remotely controllable appliances, such as lights, doors, coffee machines, temperature controls, home theatre systems, communication systems, security cameras, surveillance equipment, and the like. Controlled appliances typically either include integrated circuitry through which operative states can be controlled, or are coupled to control modules, such as an X10™ module from X10 Wireless Technology, Inc. of Seattle, Wash. Often, controlled appliances can be manipulated using remote control units and/or control panels. Further, controlled appliances can be centrally controlled by a computer executing appliance automation software. For example, a smart home can provide automatic timed control of lights and appliances. Additionally, a smart home can allow a homeowner to remotely monitor and/or control household devices, such as doors, windows, thermostats, consumer electronics, or the like. Smart home products can also provide a range of intelligent security functions.
Given the convenience of controlling one's home from a central location, and the enhanced security features, smart home technology is ideally suited for individuals who suffer from diminished mental capacities or are physically challenged. For example, individuals experiencing fading sensory and cognitive skills, such as the elderly, commonly forget to close a door or window or turn off an appliance. Further, an elderly person may desire the ability to open a curtain to let in light upon waking without having to rise from bed, or conversely, to close the curtain at bedtime. Moreover, automatic notification of emergency events, such as a water leak, allows an elderly person to seek help upon detection, reducing the probability of severe damage or injury.
Individuals suffering from physical or mental challenges, such as elderly persons, commonly require a caregiver to provide assistance with daily activities. However, in cases where the elderly person is high functioning and desires to maximize independence, a fulltime, onsite caregiver can be unnecessary, costly, and/or intrusive. Thus, the elder may prefer a remote caregiver who can assist the elder only when the elder asks for, or requires, help.
Conventional methods of providing remote care typically employ the use of portable communication devices. However, typical portable communication devices, such as pagers and cell phones, are limited. For example, in the case of a paging device, the elder must wait for a call back from the caregiver. Similarly, in the case of a cell phone, the caregiver may be out of range or unavailable, requiring the elder to leave a message and wait until the caregiver responds.
A further shortcoming of conventional remote care is the inability of the caregiver to make an immediate visual assessment of the elder's condition. For instance, in order to determine the elder's condition upon receiving a request for help, the caregiver must typically converse with the elder telephonically, which can be problematic if the elder is unable to speak or physically get to a phone. Further, the caregiver often must physically travel to the location of the elder to determine the nature of the help request, which can delay necessary treatment for the elder. The inability of the caregiver to have immediate knowledge of the elder's condition may result in the caregiver underestimating the gravity of the elder's condition. Conversely, the caregiver may overestimate the severity of the elder's condition, which may result in unnecessary and costly calls to emergency personnel, such as the fire department, ambulance, or the like.
Additionally, an elder may need a caregiver to unobtrusively check in on the elder from time to time. Optimally, the caregiver should be able to observe the elder without causing a disruption in the elder's day. However, conventional human surveillance mechanisms have many shortcomings. For example, typical remote viewing mechanisms, such as a monitor or dedicated display screen, are not portable and are operable only at a fixed location.
Most first-generation pervasive space prototypes in existence now are the result of massive ad-hoc system integration. Introducing a new device to the environment is a laborious process. After the initial decision on which particular component to purchase, the smart space developers must research the device's characteristics and operation, determining how to configure it and interface with it. The device must then somehow be connected and physically integrated into the space. Any applications using the new device must be written with knowledge of the resources assigned to connect the device, signals to query and control the device, and the meaning of any signals returned. Finally, tedious and repeated testing is required to guard against errors or indeterminate behavior that could occur if, for example, applications make conflicting requests of devices, or if devices or connection resources themselves conflict. Any change in deployed devices or applications requires repeating the process. This is the problem with conventional integrated pervasive spaces.
Pervasive computing environments such as smart spaces require a mechanism to integrate, manage and use numerous and heterogeneous sensors and actuators. There has been a dramatic increase during recent years in the number of sensor platforms in development or commercially available. One of these has been the Mote family, developed by the University of California at Berkeley as part of the SMART DUST™ project. Motes such as the MICA™, MICA2™, and MICA2DOT™ are available commercially from Crossbow Technology, Inc., San Jose, Calif. Some versions of the platform, such as MICA2™, offer limited modularity in the form of daughter cards, containing different sensor arrays, which can be plugged into the platform. Other versions lack this modularity. For example, TELOWS™, as developed by the SMART DUST™ team, is a completely integrated platform based on the TI MSP430™ microcontroller. (J. Polastre, R. Szewczyk, and D. Culler, “Telow: Enabling ultra-low power wireless research,” in Proceedings of the 4th Intl. Conf. on Information Processing in Sensor Networks, April, 2005.) It offers higher performance and consumes less power than other Mote platforms, but comes at a higher cost, and the available sensors are integrated into the device and cannot be changed by users.
Motes are currently the de facto standard platform for sensor networks. Although the Mote was primarily developed for use in wireless ad-hoc networks for applications such as remote monitoring, researchers in many unrelated areas have used Mote primarily because of its commercial availability and its ability to integrate numerous sensors into a system. Many groups are working with Motes either as the basis for other projects or to further the sensor platform itself. For example, Intel and Berkeley have worked together on iMOTE™, a high-power Bluetooth-enabled version of the wireless sensor node. (L. Nachman, R. Kling, J. Huang and V. Hummel, “The Intel mote platform: a Bluetooth-based sensor network for industrial monitoring,” in Proceedings of the 4th Intl. Conf. on Information Processing in Sensor Networks, April, 2005.) An another example, College of the Atlantic collaborated with Berkeley to use wireless sensor networks for habitat monitoring on Great Duck Island. (A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler and J. Anderson, “Wireless sensor networks for habitat monitoring,” in Proceedings of 1st ACM Intl. Workshop on Wireless Sensor Networks and Applications, pp. 88-97, September 2002.)
PHIDGETS™, developed by the University of Calgary, is another widely used, commercially available platform. (S. Greenberg and C. Fitchett, “Phidgets: easy development of physical interfaces through physical widgets,” in Proceedings of 14th ACM Symp. on User Interface Software and Technology, pp. 209-218, November 2001.) The PHIDGETS™ support a large variety of sensors and actuators. However, they are not fully modular, and they only support communication to a Windows desktop computer via USB, which leads to scalability problems.
Some groups have worked on creating a more modular sensor network platform. The CUBE™, developed by University College Cork, (B. O'Flynn et al., “The development of a novel miniaturized modular platform for wireless sensor networks,” in Proceedings of the 4th Intl. Conf. on Information Processing in Sensor Networks, April, 2005.) and MASS™, a Sandia National Laboratory project, (N. Edmonds, D. Stark and J. Davis, “MASS: modular architecture for sensor systems,” in Proceedings of the 4th Intl. Conf. on Information Processing in Sensor Networks, April, 2005.) have modular architectures allowing users to develop applications and reconfigure platforms. However, the CUBE™ platform, for example, must be hardcoded to each device. Other sensor network platforms, such as NIMS™ (R. Pon et al., “Networked infomechanical systems: a mobile embedded networked sensor platform,” in Proceedings of the 4th Intl. Conf. on Information Processing in Sensor Networks, April, 2005.), XYZ™ (D. Lymberopoulos and A. Savvides, “XYZ: a motion-enabled power aware sensor node platform for distributed sensor network applications,” in Proceedings of the 4th Intl. Conf. on Information Processing in Sensor Networks, April, 2005.), and ECO™ (C. Park, J. Liu and P. Chou, “Eco: an ultra-compact low-power wireless sensor node for real-time motion monitoring,” in Proceedings of the 4th Intl. Conf. on Information Processing in Sensor Networks, April, 2005.) were designed for specific applications: environmental monitoring (NIMS™, XYZ™) and health monitoring (ECO™).
The SMART-ITS™, developed jointly by Lancaster University and the University of Karlsruhe, offer some features that could facilitate the development of pervasive spaces. (H. Gellerson, G. Kortuem, A. Schmidt and M. Beigl, “Physical prototyping with Smart-Its,” IEEE Pervasive Computing, vol. 3, no. 3, pp. 74-82, July-September 2004.) They have a somewhat modular hardware design and a template-based software design process, which allows rapid application development. But the SMART-ITS™ platform is still not completely modular, with an integrated processing and communication board. Furthermore, devices connected through SMART-ITS™ are constrained to a single application (running on the SMART-ITS™ hardware). This does not allow for service-rich environments in which applications can be developed using service composition.
None of the available sensor network platforms are fully adequate for the scalable development of pervasive spaces. Most of the platforms focus only on sensors, and barely touch upon the issue of actuators. In a pervasive space, actuators play as important a role as sensors, as actuators are used to influence the space. NIMS™ and XYZ™ make use of actuators, but only for the specific purpose of making the platforms mobile. PHIDGETS™ support a large number of actuators, but are constrained by scalability issues and a fixed hardware configuration.
Additionally, none of these platforms have the capability to represent automatically their connected devices as software services to programmers and users. Instead, programmers must write distributed applications that query hard-coded resources to access the devices connected to the platform. Except for the larger number of devices supported, this is no better than connecting sensors and actuators directly to the input/output (I/O) ports of a computer. It is a development method that does not scale as more devices and services are added to a smart space.
Thus, there remains a need for a modular, service-oriented sensor and actuator platform specifically designed to support the development of scalable pervasive computing spaces.